1. Field of the Invention
The present general inventive concept relates to a control system, a moving robot apparatus having the control system, and a control method thereof, and more particularly, the present general inventive concept relates to a control system, which is capable of estimating control data for a moving robot using a sensor and based on a determination of whether a necessary condition is satisfied. The present general inventive concept also relates to a moving robot apparatus having this control system, and a control method thereof.
2. Description of the Related Art
A conventional moving robot apparatus having a control system for estimating optimal robot control data using a sensor that outputs data will be described with reference to FIG. 1.
FIG. 1 is a control block diagram of a conventional moving robot apparatus. As illustrated in FIG. 1, a moving robot apparatus comprises a robot main body 100, a sensor part 200, a data selecting part 300 and a filter part 400.
The sensor part 200 may comprise one or more sensors for sensing a direction of the robot main body 100, for example, an absolute direction of the robot main body with respect to the earth's magnetic field. Data output from the sensor part 200 (hereinafter referred to as ‘sensing data’) include external disturbances introduced from ambient environments during the sensing of the direction of the robot main body 100, and measurement errors produced while the sensing data are measured.
In general, a disturbance has a colored non-Gaussian distribution and a measurement error has a white Gaussian distribution. In particular, the disturbance may distort and invalidate the sensing data output from the sensor part 200.
The data selecting part 300 uses a predetermined rule to determine whether a disturbance contained in the sensing data output from the sensor part 200 can be ignored. If the sensing data satisfy the rule, the disturbance may be determined to be ignorable, and then the sensing data are output to the filter part 400.
The filter part 400, which may be embodied by a Kalman filter, accurately estimates the direction of the robot main body 100 by using the sensing data input from the data selecting part 300. The Kalman filter can calculate an optimal value (of direction estimation) when input sensing data have a Gaussian probability distribution.
However, if the rule used by the data selecting part 300 is inaccurate, sensing data having a non-Gaussian probability distribution may be input to the filter part 400. In other words, in spite of the fact that a disturbance cannot be ignored, the disturbance may still satisfy the rule and, hence, is input to the filter part 400, which may result in an undesirable result such as an erroneous direction of the robot main body 100.
For example, assume that the sensor part 200 comprises two magnetic compasses and the data selecting part 300 makes an application of the rule that a disturbance can be ignored if sensing data output from the two magnetic compasses are the same. If sensing data containing a disturbance are output from the two magnetic compasses of the sensor part 200 and are the same by accident, the data selecting part 300 determines that the disturbance can be ignored (despite the fact that the disturbance should not be ignored), and then outputs the sensing data (containing the disturbance) to the filter part 400.
As a result, a direction of the robot main body 100 estimated by the filter part 400 may be greatly different from the desired direction of the robot main body 100 with respect to the earth's magnetic field. This may result in movement of the moving robot apparatus to an erroneous direction.
Accordingly, it is important to find an accurate rule for the data selecting part 300. However, it is difficult to find a rule to allow the data selecting part 300 to accurately determine whether or not a disturbance is contained in the sensing data.
To solve such a problem partially, there has been proposed a method of estimating sensing data indirectly using a necessary condition to be satisfied and when the sensing data are not distorted by a disturbance, as disclosed in an article authored by Woong Kwon, Kyung-Shik Roh and Hak-Kyung Sung, and titled “Particles Filter-based Heading Estimation using Magnetic Compasses for mobile Robot Navigation”, Proceedings of 2006 IEEE International Conference on Robotics and Automation. The reason for using the necessary condition is that it is not difficult to find the necessary condition satisfied when the sensing data are valid.
This method may reduce the likelihood of misjudgment as to the validity of the sensing data, however, it can not completely eliminate the likelihood of misjudgment because it uses only the necessary condition and, not a sufficient condition for the determination of validity of the sensing data.